Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events

Chiang, An‐Jen; Butte, Atul J.

doi:10.1038/clpt.2008.274

Cited by 46 publications

(30 citation statements)

References 73 publications

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“…However, the number of publicly available data reporting the effects of drugs on gene expression across disease tissues continues to grow (60). Furthermore, disease-related microarray data could be combined with other types of knowledge on drugs (61, 62) for computational prediction of drug side effects.…”

Section: Discussionmentioning

confidence: 99%

Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

et al. 2011

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The application of established drug compounds to novel therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning employ high-throughput experimental approaches to assess a compound’s potential therapeutic qualities. Here we present a systematic computational approach to predict novel therapeutic indications based on comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds yielding predicted therapeutic potentials for these drugs. We demonstrate the ability to recover many known drug and disease relationships using computationally derived therapeutic potentials, and also predict many new indications for these drugs. We experimentally validated a prediction for the anti-ulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate both in vitro and in vivo using mouse xenograft models. This novel computational method provides a novel and systematic approach to reposition established drugs to treat a wide range of human diseases.

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Section: Discussionmentioning

confidence: 99%

Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

et al. 2011

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“…This could suggest patient-specific treatment recommendations [30] so that different drugs are administered to people with distinct genetic variation underlying the same phenotype. Despite this promise, the area of biomarker development has recently been shrouded by controversy [31] as a number of published associations between putative biomarkers and target phenotypes that were based on microarray and other genomic data now appear irreproducible, possibly due to flaws in statistical analysis and interpretation.…”

Section: Promising Directions In Computational Biology For Drug Discomentioning

confidence: 99%

Novel opportunities for computational biology and sociology in drug discovery

Yao¹,

Evans²,

Rzhetsky³

2009

Trends in Biotechnology

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Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies.

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“…However, the direct connection between the targeting of metabolic and signaling pathways by drugs and the adverse drug reactions that they cause has so far not been systematically studied and is only known for individual cases [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

et al. 2011

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In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.

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Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events

Cited by 46 publications

References 73 publications

Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

Novel opportunities for computational biology and sociology in drug discovery

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

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